Previously, low-rank matrix regression methods have
been used to enable "calibrationless" parallel and
"training-free" dynamic MRI reconstruction.In this work, we present a novel low n-rank
tensor regression framework for calibrationless reconstruction of dynamic and
multi-channel MRI data, and demonstrate that previously image-domain
strategies arise as instances of this unifying model.